Markov property

for a real-valued stochastic process ,

The property that for any set of times from and any Borel set ,

(*)

with probability 1, that is, the conditional probability distribution of given coincides (almost certainly) with the conditional distribution of given . This can be interpreted as independence of the "future" and the "past" given the fixed "present" . Stochastic processes satisfying the property (*) are called Markov processes (cf. Markov process). The Markov property has (under certain additional assumptions) a stronger version, known as the "strong Markov property" . In discrete time the strong Markov property, which is always true for (Markov) sequences satisfying (*), means that for each stopping time (relative to the family of -algebras , ), with probability one

References

[1]	I.I. [I.I. Gikhman] Gihman, A.V. [A.V. Skorokhod] Skorohod, "The theory of stochastic processes" , 2 , Springer (1975) (Translated from Russian)

References

[a1]	K.L. Chung, "Markov chains with stationary transition probabilities" , Springer (1960)
[a2]	J.L. Doob, "Stochastic processes" , Wiley (1953)
[a3]	E.B. Dynkin, "Markov processes" , 1 , Springer (1965) (Translated from Russian)
[a4]	T.G. Kurtz, "Markov processes" , Wiley (1986)
[a5]	W. Feller, "An introduction to probability theory and its applications" , 1–2 , Wiley (1966)
[a6]	P. Lévy, "Processus stochastiques et mouvement Brownien" , Gauthier-Villars (1965)
[a7]	M. Loève, "Probability theory" , II , Springer (1978)